Zobrazeno 1 - 10
of 8 410
pro vyhledávání: '"Zhang, David"'
Autor:
Xie, Jinheng, Mao, Weijia, Bai, Zechen, Zhang, David Junhao, Wang, Weihao, Lin, Kevin Qinghong, Gu, Yuchao, Chen, Zhijie, Yang, Zhenheng, Shou, Mike Zheng
We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discrete) diffusion modeling to adaptively handle inputs and outputs of vario
Externí odkaz:
http://arxiv.org/abs/2408.12528
Autor:
Farkya, Saurabh, Daniels, Zachary Alan, Raghavan, Aswin, van der Wal, Gooitzen, Isnardi, Michael, Piacentino, Michael, Zhang, David
Recent advancements in sensors have led to high resolution and high data throughput at the pixel level. Simultaneously, the adoption of increasingly large (deep) neural networks (NNs) has lead to significant progress in computer vision. Currently, vi
Externí odkaz:
http://arxiv.org/abs/2408.04767
Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better denoising performance. Window transformer can use long- and sh
Externí odkaz:
http://arxiv.org/abs/2407.05709
Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification.
Externí odkaz:
http://arxiv.org/abs/2405.14506
Autor:
Wang, Shuai, Zhang, David W., Huang, Jia-Hong, Rudinac, Stevan, Kackovic, Monika, Wijnberg, Nachoem, Worring, Marcel
Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural Networks (HGNNs) has emerged as a valuable method to manage the intric
Externí odkaz:
http://arxiv.org/abs/2405.13372
Autor:
Kofinas, Miltiadis, Knyazev, Boris, Zhang, Yan, Chen, Yunlu, Burghouts, Gertjan J., Gavves, Efstratios, Snoek, Cees G. M., Zhang, David W.
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors. However, existing ap
Externí odkaz:
http://arxiv.org/abs/2403.12143
Autor:
Wu, Weijia, Li, Zhuang, Gu, Yuchao, Zhao, Rui, He, Yefei, Zhang, David Junhao, Shou, Mike Zheng, Li, Yan, Gao, Tingting, Zhang, Di
We introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control methods, DragAnything offers several advantages. Firstly, trajectory-ba
Externí odkaz:
http://arxiv.org/abs/2403.07420
Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the real world, as well as collected images by cameras suffer from noise. To
Externí odkaz:
http://arxiv.org/abs/2403.02211
Autor:
Butt, Natasha, Manczak, Blazej, Wiggers, Auke, Rainone, Corrado, Zhang, David W., Defferrard, Michaël, Cohen, Taco
Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability. However, these models still perform very poorly on benchmarks of general intelligence such as the Abstraction and Reasoning Corp
Externí odkaz:
http://arxiv.org/abs/2402.04858
Autor:
Shamsian, Aviv, Navon, Aviv, Zhang, David W., Zhang, Yan, Fetaya, Ethan, Chechik, Gal, Maron, Haggai
Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural fields (INRs, NeRFs), as well as making inferences about other types of
Externí odkaz:
http://arxiv.org/abs/2402.04081